Simulation of economic benefit prediction model for green energy manufacturing based on production process thermal energy cycle and data mining

Abstract

With the global focus on sustainable development and green manufacturing, there is an urgent need for companies to optimize their production processes to improve energy efficiency and reduce carbon emissions. An economic benefit prediction model based on thermal energy cycle and data mining in production process was developed to evaluate and optimize the economic benefit in green energy manufacturing process and provide theoretical support for enterprise decision-making. The thermal energy cycle model in the production process is studied and constructed, and its application in different production links is analyzed. Data mining technology is used to analyze historical production data to identify the key factors affecting the efficiency of thermal energy cycle. By constructing regression models and time series analysis, we predict the economic benefits under different optimization strategies. The simulation results show that by optimizing the thermal energy cycle, the energy utilization efficiency can be significantly improved, the production cost can be reduced, and the environmental impact can be reduced. Therefore, the combination of heat cycle optimization and data mining provides an effective economic benefit prediction tool for green energy manufacturing. Enterprises in the implementation of green manufacturing, should pay attention to the improvement of heat energy cycle, in order to achieve higher economic and environmental benefits, to contribute to sustainable development.